Project similarity measures for collaborative filtering-based effort estimation: Review and empirical study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F25%3A63597898" target="_blank" >RIV/70883521:28140/25:63597898 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1877050925030807?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1877050925030807?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2025.09.407" target="_blank" >10.1016/j.procs.2025.09.407</a>

Result language

angličtina

Original language name

Project similarity measures for collaborative filtering-based effort estimation: Review and empirical study

Original language description

As software project development becomes increasingly complex, accurate effort estimation is essential for successful delivery. This study investigates the impact of similarity measures on estimation accuracy within the Neighborhood-Based Collaborative Filtering for Effort Estimation (NCFEE) context. We analyzed the performance of 17 similarity measures using benchmark datasets, specifically fpa_china and fpa_isbsg. Effectiveness was assessed through Root Mean Squared Error (RMSE) to quantify prediction accuracy, supplemented by effect size analysis to gauge the practical significance of observed differences. The results demonstrate that Jaccard-based measures (JAC, DiceJAC, and TanimotoJAC) consistently achieved the lowest RMSE values, indicating their strong ability to capture effort-related similarities by focusing on overlapping project features. Effect size analysis confirmed that these performance advantages are highly practically significant. Furthermore, the optimal number of nearest neighbors varied between datasets, with effect sizes highlighting the substantial impact of dataset characteristics on model performance. These findings underscore the importance of selecting appropriate similarity measures, particularly Jaccard-based approaches, to enhance the effectiveness of NCFEE. © 2025 Elsevier B.V.. All rights reserved.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Procedia Computer Science

ISBN

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ISSN

1877-0509

e-ISSN

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Number of pages

10

Pages from-to

2848-2857

Publisher name

Elsevier B.V.

Place of publication

Amsterdam

Event location

Osaka

Event date

Sep 10, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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